Your phone's voice assistant works instantly and privately because the AI brain lives on your device, not in a data center.

RunAnywhere's SDK enables developers to build fully on-device voice agents by bundling streaming speech-to-text (Whisper, Zipformer, Parakeet), text-to-speech (Piper, Kokoro, Matcha), and voice activity detection (Silero) models directly into mobile applications. The entire inference pipeline—from microphone input to natural language response—runs locally on the device's GPU or neural engine, achieving sub-200ms latency without any network dependency. This architecture eliminates cloud API costs, removes privacy risks associated with transmitting audio data, and enables voice-powered experiences in offline or low-connectivity environments such as healthcare, field operations, and regulated industries. The control plane allows OTA model updates so voice models can be improved without app store resubmissions.

It's like having a personal translator living in your pocket who never gossips about your conversations to anyone.

Employees can ask questions about sensitive company documents on their phone and get instant answers without any data ever leaving the device.

RunAnywhere provides a fully on-device Retrieval-Augmented Generation (RAG) pipeline that combines hybrid vector embeddings (Snowflake) with BM25 keyword retrieval to index and query local documents directly on mobile or edge devices. Developers can build enterprise copilot applications where users load PDFs, manuals, or compliance documents onto their device, and the local LLM (Qwen3, Llama 3.2, SmolLM2) answers questions grounded in those documents with cited passages. Because the entire pipeline—embedding generation, index construction, retrieval, and LLM inference—runs on-device via MetalRT or llama.cpp, no proprietary or regulated data is transmitted externally. This is critical for industries like healthcare, legal, finance, and defense where data residency and compliance requirements prohibit cloud processing. The control plane enables fleet-wide model and index updates via OTA delivery.

It's like giving every employee a photographic-memory research assistant who's sworn to secrecy and works without Wi-Fi.

The system automatically decides whether to run AI on your phone or in the cloud based on rules you set, saving money and keeping things fast.

RunAnywhere's policy-based routing engine allows developers and platform teams to define custom rules that dynamically determine whether each AI inference request is handled on-device or routed to a cloud endpoint. Policies can be configured based on device capability (GPU, RAM, battery), model complexity, network availability, latency requirements, data sensitivity, and cost thresholds. For example, a healthcare app might enforce that all patient-related queries run locally for HIPAA compliance, while general knowledge questions fall back to a cloud LLM for higher accuracy. The control plane provides real-time analytics on inference distribution, device performance, model accuracy, and cost attribution across the entire fleet. This hybrid architecture lets companies start with cloud AI and progressively shift workloads on-device as models shrink and hardware improves, creating a smooth migration path that avoids vendor lock-in to any single cloud AI provider. Fleet-wide policy updates are pushed via OTA without requiring app updates.

It's like a smart thermostat for your AI bills—it automatically runs the cheap, local option when it can and only fires up the expensive cloud furnace when it really needs to.